Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks

Abstract

We propose a novel Computer-Aided Detection (CAD) system for pulmonary nodules using multi-view convolutional networks (ConvNets), for which discriminative features are automatically learnt from the training data. The network is fed with nodule candidates obtained by combining three candidate detectors specifically designed for solid, subsolid, and large nodules. For each candidate, a set of 2-D patches from differently oriented planes is extracted. The proposed architecture comprises multiple streams of 2-D ConvNets, for which the outputs are combined using a dedicated fusion method to get the final classification. Data augmentation and dropout are applied to avoid overfitting. On 888 scans of the publicly available LIDC-IDRI dataset, our method reaches high detection sensitivities of 85.4% and 90.1% at 1 and 4 false positives per scan, respectively. An additional evaluation on independent datasets from the ANODE09 challenge and DLCST is performed. We showed that the proposed multi-view ConvNets is highly suited to be used for false positive reduction of a CAD system.

A survey on deep learning in medical image analysis

“…As summarized in Table 2, most works employ simple Anavi et al (2015) Image retrieval Combines classical features with those from pre-trained CNN for image retrieval using SVM Bar et al (2015) Pathology detection Features from a pre-trained CNN and low level features are used to detect various diseases Anavi et al (2016) Image retrieval Continuation of Anavi et al (2015), adding age and gender as features Bar et al (2016) Pathology detection Continuation of Bar et al (2015), more experiments and adding feature selection Cicero et al (2016) Pathology detection GoogLeNet CNN detects five common abnormalities, trained and validated on a large data set Tuberculosis detection Processes entire radiographs with a pre-trained fine-tuned network with 6 convolution layers Kim and Hwang (2016) Tuberculosis detection MIL framework produces heat map of suspicious regions via deconvolution Shin et al (2016a) Pathology detection CNN detects 17 diseases, large data set (7k images), recurrent networks produce short captions Rajkomar et al (2017) Frontal/lateral classification Pre-trained CNN performs frontal/lateral classification task Yang et al (2016c) Bone suppression Cascade of CNNs at increasing resolution learns bone images from gradients of radiographs Wang et al (2016a) Nodule classification Combines classical features with CNN features from pre-trained ImageNet CNN Used a standard feature extractor and a pre-trained CNN to classify detected lesions as benign peri-fissural nodules van Detects nodules with pre-trained CNN features from orthogonal patches around candidate, classified with SVM Shen et al (2015b) Three CNNs at different scales estimate nodule malignancy scores of radiologists (LIDC-IDRI data set) Chen et al (2016e) Combines features from CNN, SDAE and classical features to characterize nodules from LIDC-IDRI data set Ciompi et al (2016) Multi-stream CNN to classify nodules into subtypes: solid, part-solid, non-solid, calcified, spiculated, perifissural Dou et al (2016b) Uses 3D CNN around nodule candidates; ranks #1 in LUNA16 nodule detection challenge Li et al (2016a) Detects nodules with 2D CNN that processes small patches around a nodule Setio et al (2016) Detects nodules with end-to-end trained multi-stream CNN with 9 patches per candidate Shen et al (2016) 3D CNN classifies volume centered on nodule as benign/malignant, results are combined to patient level prediction Sun et al (2016b) Same dataset as Shen et al (2015b)…”

Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique

“…Several articles in this issue use this approach. Setio et al [12] combine three previously developed candidate detectors for pulmonary nodules in 3D chest CT scans and extract 2D patches centered on these candidates under nine different orientations. A combination of different CNNs is used to classify each candidate.…”

Deep learning aided decision support for pulmonary nodules diagnosing: a review

“…Setio et al (19) proposed a two-dimensional multi-view CNN for false positive reduction of pulmonary nodules, for which the inputs were fed with two-dimensional patches extracted from differently oriented planes. Their approach reached sensitivities of 85.4% and 90.1% at 1 and 4 false (38), and stacked denoising autoencoder] which were based on extracting automatically generated features with traditional computer aided diagnosis (CADx) systems using hand-crafted features including density (i.e., average intensity, standard deviation, and entropy), texture (i.e., GLCM, wavelet, LBP and SIFT) and morphological features (area, circularity, and ratio of semi-axis) for lung nodule CT image diagnosis in all of the LIDC 1,018 cases (28,29).…”